@inproceedings{fedavg,
  author    = {Brendan McMahan and
               Eider Moore and
               Daniel Ramage and
               Seth Hampson and
               Blaise Ag{\"{u}}era y Arcas},
  title     = {Communication-Efficient Learning of Deep Networks from Decentralized
               Data},
  booktitle = {Proceedings of the 20th International Conference on Artificial Intelligence
               and Statistics, {AISTATS} 2017, 20-22 April 2017, Fort Lauderdale,
               FL, {USA}},
  series    = {Proceedings of Machine Learning Research},
  volume    = {54},
  pages     = {1273--1282},
  publisher = {{PMLR}},
  year      = {2017},
  url       = {http://proceedings.mlr.press/v54/mcmahan17a.html},
}

@inproceedings{scaffold,
  title={Scaffold: Stochastic controlled averaging for federated learning},
  author={Karimireddy, Sai Praneeth and Kale, Satyen and Mohri, Mehryar and Reddi, Sashank and Stich, Sebastian and Suresh, Ananda Theertha},
  booktitle={International Conference on Machine Learning},
  pages={5132--5143},
  year={2020},
  organization={PMLR}
}

@article{FedDF,
  title={Ensemble distillation for robust model fusion in federated learning},
  author={Lin, Tao and Kong, Lingjing and Stich, Sebastian U and Jaggi, Martin},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={2351--2363},
  year={2020}
}

@inproceedings{FedBE,
  author    = {Hong{-}You Chen and
               Wei{-}Lun Chao},
  title     = {FedBE: Making Bayesian Model Ensemble Applicable to Federated Learning},
  booktitle = {9th International Conference on Learning Representations, {ICLR} 2021,
               Virtual Event, Austria, May 3-7, 2021},
  year      = {2021},
}

@article{FedAvg_Momentum,
  author    = {Tzu{-}Ming Harry Hsu and
               Hang Qi and
               Matthew Brown},
  title     = {Measuring the Effects of Non-Identical Data Distribution for Federated
               Visual Classification},
  journal   = {CoRR},
  volume    = {abs/1909.06335},
  year      = {2019},
  url       = {http://arxiv.org/abs/1909.06335},
  eprinttype = {arXiv},
  eprint    = {1909.06335},
}

@inproceedings{PATE,
  author    = {Nicolas Papernot and
               Mart{\'{\i}}n Abadi and
               {\'{U}}lfar Erlingsson and
               Ian J. Goodfellow and
               Kunal Talwar},
  title     = {Semi-supervised Knowledge Transfer for Deep Learning from Private
               Training Data},
  booktitle = {5th International Conference on Learning Representations, {ICLR} 2017,
               Toulon, France, April 24-26, 2017, Conference Track Proceedings},
  publisher = {OpenReview.net},
  year      = {2017},
}

@inproceedings{FedProx,
  title={Federated optimization in heterogeneous networks},
  author={Li, Tian and Sahu, Anit Kumar and Zaheer, Manzil and Sanjabi, Maziar and Talwalkar, Ameet and Smith, Virginia},
  booktitle={Proceedings of Machine Learning and Systems},
  volume={2},
  pages={429--450},
  year={2020}
}

@article{jiang2022signds,
  title={SignDS-FL: Local Differentially Private Federated Learning with Sign-based Dimension Selection},
  author={Jiang, Xue and Zhou, Xuebing and Grossklags, Jens},
  journal={ACM Transactions on Intelligent Systems and Technology (TIST)},
  year={2022},
  publisher = {Association for Computing Machinery},
  address = {New York, USA}
}

@inproceedings{mcsherry2007mechanism,
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@article{dwork2014algorithmic,
  title={The algorithmic foundations of differential privacy},
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  volume={9},
  number={3--4},
  pages={211--407},
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}
